In [1]:
import numpy as np  #linear algebra 
import pandas as pd  #data processing
In [9]:
df=pd.read_csv(r"C:\Users\sudha\Downloads\Unemployment_Rate_upto_11_2020.csv") #read dataset
In [10]:
df.head()
Out[10]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Region.1 longitude latitude
0 Andhra Pradesh 31-01-2020 M 5.48 16635535 41.02 South 15.9129 79.74
1 Andhra Pradesh 29-02-2020 M 5.83 16545652 40.90 South 15.9129 79.74
2 Andhra Pradesh 31-03-2020 M 5.79 15881197 39.18 South 15.9129 79.74
3 Andhra Pradesh 30-04-2020 M 20.51 11336911 33.10 South 15.9129 79.74
4 Andhra Pradesh 31-05-2020 M 17.43 12988845 36.46 South 15.9129 79.74
In [11]:
df.tail()
Out[11]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Region.1 longitude latitude
262 West Bengal 30-06-2020 M 7.29 30726310 40.39 East 22.9868 87.855
263 West Bengal 31-07-2020 M 6.83 35372506 46.17 East 22.9868 87.855
264 West Bengal 31-08-2020 M 14.87 33298644 47.48 East 22.9868 87.855
265 West Bengal 30-09-2020 M 9.35 35707239 47.73 East 22.9868 87.855
266 West Bengal 31-10-2020 M 9.98 33962549 45.63 East 22.9868 87.855
In [12]:
#returns tuple of shape (Rows, columns) of dataframe
df.shape
Out[12]:
(267, 9)
In [13]:
#prints information about the dataframe
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 267 entries, 0 to 266
Data columns (total 9 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   Region                                    267 non-null    object 
 1    Date                                     267 non-null    object 
 2    Frequency                                267 non-null    object 
 3    Estimated Unemployment Rate (%)          267 non-null    float64
 4    Estimated Employed                       267 non-null    int64  
 5    Estimated Labour Participation Rate (%)  267 non-null    float64
 6   Region.1                                  267 non-null    object 
 7   longitude                                 267 non-null    float64
 8   latitude                                  267 non-null    float64
dtypes: float64(4), int64(1), object(4)
memory usage: 18.9+ KB
In [14]:
#returns numerical description of the data in the dataframe
df.describe()
Out[14]:
Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) longitude latitude
count 267.000000 2.670000e+02 267.000000 267.000000 267.000000
mean 12.236929 1.396211e+07 41.681573 22.826048 80.532425
std 10.803283 1.336632e+07 7.845419 6.270731 5.831738
min 0.500000 1.175420e+05 16.770000 10.850500 71.192400
25% 4.845000 2.838930e+06 37.265000 18.112400 76.085600
50% 9.650000 9.732417e+06 40.390000 23.610200 79.019300
75% 16.755000 2.187869e+07 44.055000 27.278400 85.279900
max 75.850000 5.943376e+07 69.690000 33.778200 92.937600
In [15]:
x = df['Region'] #plotting column 'Region' on x-axis
In [16]:
x
Out[16]:
0      Andhra Pradesh
1      Andhra Pradesh
2      Andhra Pradesh
3      Andhra Pradesh
4      Andhra Pradesh
            ...      
262       West Bengal
263       West Bengal
264       West Bengal
265       West Bengal
266       West Bengal
Name: Region, Length: 267, dtype: object
In [17]:
y=df[' Estimated Unemployment Rate (%)']
In [18]:
y
Out[18]:
0       5.48
1       5.83
2       5.79
3      20.51
4      17.43
       ...  
262     7.29
263     6.83
264    14.87
265     9.35
266     9.98
Name:  Estimated Unemployment Rate (%), Length: 267, dtype: float64
In [19]:
df2=df.iloc[:,3]
In [20]:
df2
Out[20]:
0       5.48
1       5.83
2       5.79
3      20.51
4      17.43
       ...  
262     7.29
263     6.83
264    14.87
265     9.35
266     9.98
Name:  Estimated Unemployment Rate (%), Length: 267, dtype: float64
In [10]:
import plotly.express as px
import matplotlib.pyplot as plt
import pandas as pd
In [11]:
df=pd.read_csv(r"C:\Users\sudha\Downloads\Unemployment_Rate_upto_11_2020.csv") #read dataset
In [12]:
fg = px.bar(df,x='Region',y=' Estimated Unemployment Rate (%)',color='Region',
            title='Unemploymeny Rate (State Wise) by Bar Graph',template='plotly')
fg.update_layout(xaxis={'categoryorder':'total descending'})
fg.show()
In [13]:
fg = px.bar(df,x='Region.1',y=' Estimated Unemployment Rate (%)',color='Region',
            title='Unemploymeny Rate (Region Wise) by Bar Graph',template='plotly')
fg.update_layout(xaxis={'categoryorder':'total descending'})
fg.show()
In [14]:
fg = px.box(df,x='Region',y=' Estimated Unemployment Rate (%)',color='Region',
            title='Unemploymeny Rate (Statewise) by Box Plot',template='plotly')
fg.update_layout(xaxis={'categoryorder':'total descending'})
fg.show()
In [15]:
fg = px.scatter(df,x='Region',y=' Estimated Unemployment Rate (%)',color='Region',
                title='Unemploymeny Rate (Statewise) by Scatter Plot',template='plotly')
fg.update_layout(xaxis={'categoryorder':'total descending'})
fg.show()
In [16]:
fg = px.histogram(df,x='Region',y=' Estimated Unemployment Rate (%)',color='Region',
                  title='Unemploymeny Rate (Statewise) by Histogram',template='plotly')
fg.update_layout(xaxis={'categoryorder':'total descending'})
fg.show()
In [ ]: